C. T. Hansen, C. Ritz, D. Gerhard, J. E. Jensen, and J. C. Streibig, Re-evaluation of groundwater monitoring data for glyphosate and bentazone by taking detection limits into account, Science of The Total Environment, vol.536, pp.68-71, 2015.
DOI : 10.1016/j.scitotenv.2015.07.047

M. J. Hedegaard, E. Arvin, C. B. Corfitzen, and H. J. Albrechtsen, Mecoprop (MCPP) removal in full-scale rapid sand filters at a groundwater-based waterworks, Science of The Total Environment, vol.499, pp.499-257, 2014.
DOI : 10.1016/j.scitotenv.2014.08.052

P. Palma, M. Köck-schulmeyer, P. Alvarenga, L. Ledo, I. R. Barbosa et al., Risk assessment of pesticides detected in surface water of the Alqueva reservoir (Guadiana basin, southern of Portugal), Sci. Total Environ, pp.488-489, 2014.

I. A. Idowu, R. M. Alkhaddar, and W. Atherton, Possible source term of high concentrations of mecoprop-p in leachate and water quality: impact of climate change, public use and disposal, Environmental Technology, vol.24, issue.16, pp.35-2055, 2014.
DOI : 10.1002/ps.305

M. C. Hermosin, M. J. Calderon, M. Real, and J. Cornejo, Impact of herbicides used in olive groves on waters of the Guadalquivir river basin (southern Spain), Agriculture, Ecosystems & Environment, vol.164, pp.164-229, 2013.
DOI : 10.1016/j.agee.2012.09.021

A. Masiá, J. Campo, P. Vázquez-roig, C. Blasco, and Y. Picó, Screening of currently used pesticides in water, sediments and biota of the Guadalquivir River Basin (Spain), Journal of Hazardous Materials, vol.263, pp.95-104, 2013.
DOI : 10.1016/j.jhazmat.2013.09.035

J. Tournebize, E. Passeport, C. Chaumont, C. Fesneau, A. Guenne et al., Pesticide de-contamination of surface waters as a wetland ecosystem service in agricultural landscapes, Ecological Engineering, vol.56, pp.56-51, 2013.
DOI : 10.1016/j.ecoleng.2012.06.001

B. E. Björklund, G. G. Anskjaer, M. Hansen, B. Styrishave, and B. , Analysis and environmental concentrations of the herbicide dichlobenil and its main metabolite 2,6-dichlorobenzamide (BAM): A review, Science of The Total Environment, vol.409, issue.12, pp.2343-2356, 2011.
DOI : 10.1016/j.scitotenv.2011.02.008

L. Gutowski, O. Olsson, C. Leder, and K. Kümmerer, A comparative assessment of the transformation products of S-metolachlor and its commercial product Mercantor Gold?? and their fate in the aquatic environment by employing a combination of experimental and in silico methods, Science of The Total Environment, vol.506, issue.507, pp.506-507, 2015.
DOI : 10.1016/j.scitotenv.2014.11.025

Y. Q. Zhao, P. Singleton, S. Meredithand, and G. W. Rennick, Current status of pesticides application and their residue in the water environment in Ireland, International Journal of Environmental Studies, vol.68, issue.4, pp.70-59, 2013.
DOI : 10.1016/j.proenv.2012.01.167

F. A. Oliveira, L. P. Reis, B. Soto-blanco, and M. M. Melo, (Valenciennes,??1850) fish caught in the S??o Francisco River, Brazil, Journal of Environmental Science and Health, Part B, vol.3, issue.1, pp.398-405, 2015.
DOI : 10.1016/j.vetpar.2012.10.017

N. Takakura, P. Sanders, V. Fessard, and L. L. Hégarat, In vitro combined cytotoxic effects of pesticide cocktails simultaneously found in the French diet, Food and Chemical Toxicology, vol.52, pp.52-153, 2013.
DOI : 10.1016/j.fct.2012.11.011
URL : https://hal.archives-ouvertes.fr/hal-00766599

C. Ahouangninou, M. Thibaud, P. Edorh, B. B. Sahabi, S. Onil et al., Characterization of Health and Environmental Risks of Pesticide Use in Market-Gardening in the Rural City of Tori-Bossito in Benin, West Africa, Journal of Environmental Protection, vol.03, issue.03, pp.241-248, 2012.
DOI : 10.4236/jep.2012.33030

S. Kang, N. Chang, Y. Zhao, and C. Pan, Development of a Method for the Simultaneous Determination of Six Sulfonylurea Herbicides in Wheat, Rice, and Corn by Liquid Chromatography???Tandem Mass Spectrometry, Journal of Agricultural and Food Chemistry, vol.59, issue.18, pp.59-9776, 2011.
DOI : 10.1021/jf2020073

C. Wu, Y. Luo, T. Gui, and Y. Huang, Concentrations and potential health hazards of organochlorine pesticides in shallow groundwater of Taihu Lake region, China, Science of The Total Environment, vol.470, issue.471, pp.470-471, 2014.
DOI : 10.1016/j.scitotenv.2013.10.056

M. T. Baltazar, R. J. Dinis-oliveira-de-lourdes, M. Bastos, A. M. Tsatsakis, J. A. Duarte et al., Pesticides exposure as etiological factors of Parkinson's disease and other neurodegenerative diseases???A mechanistic approach, Toxicology Letters, vol.230, issue.2, pp.230-85, 2014.
DOI : 10.1016/j.toxlet.2014.01.039

L. S. Kjeldsen, M. Ghisari, and E. C. , Bonefeld-Jørgensen, Currently used pesticides and their mixtures affect the function of sex hormone receptors and aromatase enzyme activity, Toxicol. Appl. Pharmacol, pp.272-453, 2013.

W. Zheng, H. Yu, X. Wang, W. Qu, and C. C. Accepted, Systematic review of pentachlorophenol occurrence in the A MANUSCRIPT environment and in humans in China: Not a negligible health risk due to the re-emergence of schistosomiasis, Environ. Int, pp.42-105, 2012.

W. S. Goldner, D. P. Sandler, F. Yu, J. A. Hoppin, F. Kamel et al., Pesticide Use and Thyroid Disease Among Women in the Agricultural Health Study, American Journal of Epidemiology, vol.171, issue.4, pp.171-455, 2010.
DOI : 10.1093/aje/kwp404

K. Grote, L. Niemann, B. Selzsam, W. Haider, C. Gericke et al., EPOXICONAZOLE CAUSES CHANGES IN TESTICULAR HISTOLOGY AND SPERM PRODUCTION IN THE JAPANESE QUAIL (COTURNIX COTURNIX JAPONICA), Environmental Toxicology and Chemistry, vol.27, issue.11, pp.27-2368, 2008.
DOI : 10.1897/08-048.1

W. Donald, C. T. Michelle, and D. Krewski, A Systematic Review and Meta-analysis of Childhood Leukemia and Parental Occupational Pesticide Exposure, Environ. Health Perspect, vol.117, pp.1505-1513, 2009.

M. P. Montgomery, F. Kamel, T. M. Saldana, M. C. Alavanja, and D. P. Sandler, Incident diabetes and pesticide exposure among licensed pesticide applicators: Agricultural Health Study, Am. J. Epidemiol, pp.67-1235, 1993.

A. A. Lagunin, A. V. Zakharov, D. A. Filimonov, and V. V. Poroikov, A new approach to QSAR modelling of acute toxicity???, SAR and QSAR in Environmental Research, vol.41, issue.3-4, pp.285-298, 2007.
DOI : 10.1080/105172397243079

D. Cao, J. Dong, N. Wang, M. Wen, B. Deng et al., In silico toxicity prediction of chemicals from EPA toxicity database by kernel fusion-based support vector machines, Chemometrics and Intelligent Laboratory Systems, vol.146, pp.146-494, 2015.
DOI : 10.1016/j.chemolab.2015.07.009

S. Yousefinejad and B. Hemmateenejad, Chemometrics tools in QSAR/QSPR studies: A historical perspective, Chemometrics and Intelligent Laboratory Systems, vol.149, 2015.
DOI : 10.1016/j.chemolab.2015.06.016

J. Devillers and H. Devillers, Prediction of acute mammalian toxicity from QSARs and interspecies correlations, SAR and QSAR in Environmental Research, vol.47, issue.5-6, pp.467-500, 2009.
DOI : 10.1080/10629360600934044

E. M. Hulzebos and R. Posthumus, : gatekeepers against risk on chemicals?, SAR and QSAR in Environmental Research, vol.14, issue.4, pp.285-316, 2003.
DOI : 10.1080/1062936032000101510

M. Yang and X. Zhang, Comparative developmental toxicity of new aromatic halogenated DBPs in a chlorinated saline sewage effluent to the marine polychaete Platynereis dumerilii, Environ. Sci. Technol, pp.47-10868, 2013.

G. Om-praba and D. Velmurugan, Quantitative structure-activity relationship of some pesticides, Indian J. Biochem. Biophys, vol.43, pp.154-159, 2006.

K. Roy, S. Kar, and P. Ambure, On a simple approach for determining applicability domain of QSAR models, Chemometrics and Intelligent Laboratory Systems, vol.145, pp.22-29, 2015.
DOI : 10.1016/j.chemolab.2015.04.013

P. Ruiz, G. Begluitti, T. Tincher, J. Wheeler, and M. Mumtaz, Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products, Molecules, vol.17, issue.12, pp.17-8982, 2012.
DOI : 10.3390/molecules17088982

K. Roy, S. Kar, and R. N. Das, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, pp.1-46, 2015.

K. Roy, S. Kar, and R. N. Das, A Primer on QSAR/QSPR Modeling: Fundamental Concepts, pp.1-35, 2015.
DOI : 10.1007/978-3-319-17281-1

M. T. Cronin, The Current Status and Future Applicability of Quantitative Structure-activity Relationships (QSARs) in Predicting Toxicity, Altern. Lab. Anim, vol.30, pp.81-84, 2002.

J. R. Ruiz, J. Ramirez-lechuga, F. B. Ortega, J. Castro-pinero, J. M. Benitez et al., Artificial neural network-based equation for estimating VO 2max from the 20 m shuttle run test in adolescents, Artif. Intell. Med, pp.44-233, 2008.

A. Speck-planche, V. V. Kleandrova, F. Luan, and M. N. Cordeiro, Predicting multiple ecotoxicological profiles in agrochemical fungicides: A multi-species chemoinformatic approach, Ecotoxicology and Environmental Safety, vol.80, pp.80-308, 2012.
DOI : 10.1016/j.ecoenv.2012.03.018

F. J. Prado-prado, F. Borges, L. G. Perez-montoto, and H. Gonzalez-diaz, Multi-target spectral moment: QSAR for antifungal drugs vs. different fungi species, European Journal of Medicinal Chemistry, vol.44, issue.10, pp.44-4051, 2009.
DOI : 10.1016/j.ejmech.2009.04.040

A. A. Toropov and E. Benfenati, QSAR models of quail dietary toxicity based on the graph of atomic orbitals, Bioorganic & Medicinal Chemistry Letters, vol.16, issue.7, pp.1941-1943, 2006.
DOI : 10.1016/j.bmcl.2005.12.085

A. Schrage, K. Hempel, M. Schulz, S. N. Kolle, B. Van-ravenzwaay et al., Refinement and Reduction of Acute Oral Toxicity Testing: A Critical Review of the Use of Cytotoxicity Data, Altern. Lab. Anim, pp.39-273, 2011.

M. T. Cronin and T. W. Schultz, Pitfalls in QSAR, Journal of Molecular Structure: THEOCHEM, vol.622, issue.1-2, pp.39-51, 2003.
DOI : 10.1016/S0166-1280(02)00616-4

C. Nantasenamat, T. Tantimongcolwat, T. Naenna, C. Isarankura-na-ayudhya, and V. , Prediction of selectivity index of pentachlorophenol-imprinted polymers, EXCLI J, vol.5, pp.150-163, 2006.

D. A. Konovalov, N. Sim, E. Deconinck, Y. V. Heyden, and D. Coomans, Statistical confidence for variable selection in QSAR models, J. Chem. Inf. Model, pp.48-370, 2008.

A. S. Reddy, S. Kumar, and R. Garg, Hybrid-genetic algorithm based descriptor optimization and QSAR models for predicting the biological activity of Tipranavir analogs for HIV protease inhibition, Journal of Molecular Graphics and Modelling, vol.28, issue.8, pp.28-852, 2010.
DOI : 10.1016/j.jmgm.2010.03.005

L. Xu and W. J. Zhang, Comparison of different methods for variable selection, Analytica Chimica Acta, vol.446, issue.1-2, pp.477-483, 2001.
DOI : 10.1016/S0003-2670(01)01271-5

A. Tropsha, P. Gramatica, and V. K. Gombar, The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models, QSAR & Combinatorial Science, vol.38, issue.1, pp.69-77, 2003.
DOI : 10.1002/qsar.200390007

D. M. Hawkins, The Problem of Overfitting, Journal of Chemical Information and Computer Sciences, vol.44, issue.1, pp.1-12, 2004.
DOI : 10.1021/ci0342472

Q. Shen, J. H. Jiang, C. X. Jiao, W. Q. Lin, G. L. Shen et al., Hybridized particle swarm algorithm for adaptive structure training of multilayer feed-forward neural net-work: QSAR studies of bioactivity of organic compounds, J. Comput. Chem, pp.25-1726, 2004.

P. Izadyan, M. H. Fatemi, and M. Izadyan, Elicitation of the most important structural properties of ionic liquids affecting ecotoxicity in limnic green algae; a QSAR approach, Ecotoxicology and Environmental Safety, vol.87, pp.42-48, 2013.
DOI : 10.1016/j.ecoenv.2012.10.005

O. Prakash, F. Khan, R. S. Sangwan, and L. Misra, ANN-QSAR Model for Virtual Screening of Androstenedione C-Skeleton Containing Phytomolecules and Analogues for Cytotoxic Activity Against Human Breast Cancer Cell Line MCF-7, Combinatorial Chemistry & High Throughput Screening, vol.16, issue.1, pp.57-72, 2013.
DOI : 10.2174/1386207311316010008

R. Wang, J. Jiang, Y. Pan, H. Cao, and Y. Cui, Prediction of impact sensitivity of nitro energetic compounds by neural network based on electrotopological-state indices, Journal of Hazardous Materials, vol.166, issue.1, pp.166-155, 2009.
DOI : 10.1016/j.jhazmat.2008.11.005

P. K. Ojha, I. Mitra, R. N. Das, and K. Roy, Further exploring rm2 metrics for validation of QSPR models, Chemometrics and Intelligent Laboratory Systems, vol.107, issue.1, pp.194-205, 2011.
DOI : 10.1016/j.chemolab.2011.03.011

I. Mitra, A. Saha, and K. Roy, Chemometric QSAR Modeling and In Silico Design of Antioxidant NO Donor Phenols, Scientia Pharmaceutica, vol.79, issue.1, pp.31-57, 2011.
DOI : 10.3797/scipharm.1011-02

J. W. Chen, X. H. Li, H. Y. Yu, Y. N. Wang, and X. L. Qiao, Progress and perspectives of quantitative structure-activity relationships used for ecological risk assessment of toxic organic compounds, Science in China Series B: Chemistry, vol.58, issue.5???6, pp.593-606, 2008.
DOI : 10.1007/s11426-008-0076-6

E. M. De-haas, T. Eikelboom, and T. Bouwman, Internal and external validation of the long-term QSARs for neutral organics to fish from ECOSAR???, SAR and QSAR in Environmental Research, vol.41, issue.5-6, pp.545-559, 2011.
DOI : 10.1520/STP13178S

T. W. Roberts, D. T. Schultz, J. J. Stanton, W. Van-de-sandt, G. Tong et al., Current status of methods for defining the applicability domain of (quantitative) structure?activity relationships, Altern. Lab. Anim, pp.33-155, 2005.

A. Habibi-yangjeh and M. Danandeh-jenagharad, Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis, Monatshefte f??r Chemie - Chemical Monthly, vol.5, issue.11, pp.1279-1288, 2009.
DOI : 10.1007/s00706-009-0185-8

G. Tugcu, M. S. Türker, M. Vracko, M. Novic, and N. Minovski, QSTR modelling of the acute toxicity of pharmaceuticals to fish, SAR and QSAR in Environmental Research, vol.97, issue.3-4, pp.297-310, 2012.
DOI : 10.1016/j.chemosphere.2007.12.006

H. Du, J. Wang, Z. Hu, X. Yao, and X. Zhang, Prediction of Fungicidal Activities of Rice Blast Disease Based on Least-Squares Support Vector Machines and Project Pursuit Regression, Journal of Agricultural and Food Chemistry, vol.56, issue.22, pp.10785-10792, 2008.
DOI : 10.1021/jf8022194

P. R. Duchowicz, J. Marrugo, J. Erlinda, V. Ortiz, E. A. Castro et al., Vivas-Reyes, QSAR study for the fish toxicity of benzene derivatives, J. Argentine Chem. Soc, pp.97-116, 2009.

N. Tan, P. Li, H. Rao, Z. Li, and X. Li, Prediction of the acute toxicity of chemical compounds to the fathead minnow by machine learning approaches, Chemometrics and Intelligent Laboratory Systems, vol.100, issue.1, pp.66-73, 2010.
DOI : 10.1016/j.chemolab.2009.11.002

J. Xu, L. Zhu, D. Fang, L. Wang, S. Xiao et al., QSPR studies of impact sensitivity of nitro energetic compounds using three-dimensional descriptors, Journal of Molecular Graphics and Modelling, vol.36, pp.36-46, 2012.
DOI : 10.1016/j.jmgm.2012.03.002

G. D. Garson, Interpreting neural network connection weights, Artif. Intell. Expert, vol.6, pp.47-51, 1991.

A. T. Goh, Back-propagation neural networks for modeling complex systems, Artificial Intelligence in Engineering, vol.9, issue.3, pp.143-151, 1995.
DOI : 10.1016/0954-1810(94)00011-S

J. D. Gough and L. H. Hall, Modeling the toxicity of amide herbicides using the electrotopological state, Environmental Toxicology and Chemistry, vol.I, issue.110, pp.1069-1075, 1999.
DOI : 10.1002/etc.5620180535

D. Zakarya, E. M. Larfaoui, A. Boulaamail, and T. Lakhlifi, Analysis of Structure-Toxicity Relationships for A Series of Amide Herbicides Using Statistical Methods and Neural Network, SAR and QSAR in Environmental Research, vol.4, issue.4, pp.269-279, 1996.
DOI : 10.1016/0223-5234(94)90058-2

A. Can, I. Yildiz, and G. Guvendik, The determination of toxicities of sulphonylurea and phenylurea herbicides with quantitative structure???toxicity relationship (QSTR) studies, Environmental Toxicology and Pharmacology, vol.35, issue.3, pp.369-379, 2013.
DOI : 10.1016/j.etap.2013.02.001

M. Nendza, B. Dittrich, A. Wenzel, and W. Klein, Predictive QSAR models for estimating ecotoxic hazard of plant-protecting agents: target and non-target toxicity, Science of The Total Environment, vol.109, issue.110, pp.110-527, 1991.
DOI : 10.1016/0048-9697(91)90206-T

J. Devillers, Prediction of mammalian toxicity of organophosphorus pesticides from QSTR modeling, SAR and QSAR in Environmental Research, vol.15, issue.5-6, pp.501-510, 2004.
DOI : 10.1080/10629360412331297443